Towards AI-Based Traffic Counting System with Edge Computing
The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5551976 |
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| _version_ | 1832550884530192384 |
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| author | Duc-Liem Dinh Hong-Nam Nguyen Huy-Tan Thai Kim-Hung Le |
| author_facet | Duc-Liem Dinh Hong-Nam Nguyen Huy-Tan Thai Kim-Hung Le |
| author_sort | Duc-Liem Dinh |
| collection | DOAJ |
| description | The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detection models to perform vehicle detecting, tracking, and counting. First, a vehicle detection dataset (VDD) representing traffic conditions in Vietnam was created. Several deep learning models for VDD were then examined on two different edge device types. Using this detection, we presented a lightweight counting method seamlessly combining with a traditional tracking method to increase counting accuracy. Finally, the traffic flow information is obtained based on counted vehicle categories and their directions. The experiment results clearly indicate that the proposed system achieves the top inference speed at around 26.8 frames per second (FPS) with 92.1% accuracy on the VDD. This proves that our proposal is capable of producing high-accuracy traffic flow information and can be applicable to ITS in order to reduce labor-intensive tasks in traffic management. |
| format | Article |
| id | doaj-art-1813f7e06e824ee2b9a5442f19d96cb5 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1813f7e06e824ee2b9a5442f19d96cb52025-02-03T06:05:32ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55519765551976Towards AI-Based Traffic Counting System with Edge ComputingDuc-Liem Dinh0Hong-Nam Nguyen1Huy-Tan Thai2Kim-Hung Le3University of Information Technology-VNU-HCM, Ho Chi Minh City, VietnamUniversity of Information Technology-VNU-HCM, Ho Chi Minh City, VietnamUniversity of Information Technology-VNU-HCM, Ho Chi Minh City, VietnamUniversity of Information Technology-VNU-HCM, Ho Chi Minh City, VietnamThe recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detection models to perform vehicle detecting, tracking, and counting. First, a vehicle detection dataset (VDD) representing traffic conditions in Vietnam was created. Several deep learning models for VDD were then examined on two different edge device types. Using this detection, we presented a lightweight counting method seamlessly combining with a traditional tracking method to increase counting accuracy. Finally, the traffic flow information is obtained based on counted vehicle categories and their directions. The experiment results clearly indicate that the proposed system achieves the top inference speed at around 26.8 frames per second (FPS) with 92.1% accuracy on the VDD. This proves that our proposal is capable of producing high-accuracy traffic flow information and can be applicable to ITS in order to reduce labor-intensive tasks in traffic management.http://dx.doi.org/10.1155/2021/5551976 |
| spellingShingle | Duc-Liem Dinh Hong-Nam Nguyen Huy-Tan Thai Kim-Hung Le Towards AI-Based Traffic Counting System with Edge Computing Journal of Advanced Transportation |
| title | Towards AI-Based Traffic Counting System with Edge Computing |
| title_full | Towards AI-Based Traffic Counting System with Edge Computing |
| title_fullStr | Towards AI-Based Traffic Counting System with Edge Computing |
| title_full_unstemmed | Towards AI-Based Traffic Counting System with Edge Computing |
| title_short | Towards AI-Based Traffic Counting System with Edge Computing |
| title_sort | towards ai based traffic counting system with edge computing |
| url | http://dx.doi.org/10.1155/2021/5551976 |
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